---
title: Introduction
description: Headroom is the context optimization layer for LLM applications. Compress tool outputs, DB results, file reads, and RAG results before they reach the model. Same answers, fraction of the tokens.
---

<StatsSection />

Headroom compresses everything your AI agent reads -- tool outputs, database results, file reads, RAG retrievals, API responses -- before it reaches the LLM. The model sees less noise, responds faster, and costs less.

## Quick preview

<Tabs groupId="lang" items={['TypeScript', 'Python']}>
<Tab value="TypeScript">
```ts twoslash
import { compress } from 'headroom-ai';

const messages = [
  { role: 'user' as const, content: 'Analyze these results' },
];

const result = await compress(messages, { model: 'gpt-4o' });
console.log(`Saved ${result.tokensSaved} tokens (${(result.compressionRatio * 100).toFixed(0)}%)`);
```
</Tab>
<Tab value="Python">
```python
from headroom import compress

result = compress(messages, model="gpt-4o")
response = client.messages.create(
    model="gpt-4o",
    messages=result.messages,
)
print(f"Saved {result.tokens_saved} tokens ({result.compression_ratio:.0%})")
```
</Tab>
</Tabs>

## Community stats

<LiveStats />

## What gets compressed

| Content type | What happens | Typical savings |
|---|---|---|
| JSON arrays (tool outputs) | Statistical analysis keeps errors, anomalies, boundaries | 70--90% |
| Source code | AST-aware compression preserves signatures, collapses bodies | 40--70% |
| Build/test logs | Keeps failures and errors, drops passing noise | 80--95% |
| Search results | Ranks by relevance, keeps top matches | 60--80% |
| Plain text | ModernBERT token classification removes redundancy | 30--50% |
| Git diffs | Preserves change hunks, drops unchanged context | 40--60% |
| Images | ML router selects optimal resize/quality tradeoff | 40--90% |

## Where Headroom fits

```
Your Agent / App
    |
    |  tool outputs, logs, DB reads, RAG results, file reads, API responses
    v
 Headroom  <-- proxy, Python library, TS SDK, or framework integration
    |
    v
 LLM Provider  (OpenAI, Anthropic, Google, Bedrock, 100+ via LiteLLM)
```

Headroom works as a **transparent proxy** (zero code changes), a **Python function** (`compress()`), a **TypeScript function** (`compress()`), or a **framework integration** (LangChain, Agno, Strands, LiteLLM, Vercel AI SDK, MCP).

## Real-world results

**100 production log entries. One critical error buried at position 67.**

| Metric | Baseline | Headroom |
|---|---|---|
| Input tokens | 10,144 | 1,260 |
| Correct answers | **4/4** | **4/4** |

87.6% fewer tokens. Same answer. The FATAL error was automatically preserved -- not by keyword matching, but by statistical analysis of field variance.

| Scenario | Before | After | Savings |
|---|---|---|---|
| Code search (100 results) | 17,765 | 1,408 | **92%** |
| SRE incident debugging | 65,694 | 5,118 | **92%** |
| Codebase exploration | 78,502 | 41,254 | **47%** |
| GitHub issue triage | 54,174 | 14,761 | **73%** |

## Key Features

<KeyFeatures />

## Framework Integrations

<FrameworkIntegrations />

## Nothing is lost

Compressed content goes into the CCR store (Compress-Cache-Retrieve). The LLM gets a `headroom_retrieve` tool and can fetch full originals when it needs more detail. Compression is aggressive but reversible.

## Next steps

<Cards>
  <Card title="Quickstart" href="/docs/quickstart" />
  <Card title="Installation" href="/docs/installation" />
  <Card title="Proxy Server" href="/docs/proxy" />
  <Card title="Vercel AI SDK" href="/docs/vercel-ai-sdk" />
  <Card title="LangChain" href="/docs/langchain" />
  <Card title="How Compression Works" href="/docs/how-compression-works" />
</Cards>
